Instructions to use ylacombe/mms-spa-finetuned-argentinian-monospeaker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ylacombe/mms-spa-finetuned-argentinian-monospeaker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="ylacombe/mms-spa-finetuned-argentinian-monospeaker")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("ylacombe/mms-spa-finetuned-argentinian-monospeaker") model = AutoModelForPreTraining.from_pretrained("ylacombe/mms-spa-finetuned-argentinian-monospeaker", device_map="auto") - Transformers.js
How to use ylacombe/mms-spa-finetuned-argentinian-monospeaker with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-to-speech', 'ylacombe/mms-spa-finetuned-argentinian-monospeaker'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4982f965f6a5c6ce5d6e56773f3523bb2fe9aeed8ae0c13fc30dc468362b3ab4
- Size of remote file:
- 332 MB
- SHA256:
- 0f171ec0d8593c0fc598a318c7326bb19a6c27417523a06a2c2b293931c61d56
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